Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Statistical and Algorithmic Investing Strategies for Everyone

Python library for portfolio optimization built on top of scikit-learn

Portfolio optimization and back-testing.

Portfolio optimization with deep learning.

An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎

An open source library for portfolio optimisation

Fast and scalable construction of risk parity portfolios

Entropy Pooling views and stress-testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.

Q-Learning Based Cryptocurrency Trader and Portfolio Optimizer for the Poloniex Exchange

Оптимизация долгосрочного портфеля акций

Financial pipeline for the data-driven investor to research, develop and deploy robust strategies. Big Data ingestion, risk factor modeling, stock screening, portfolio optimization, and broker API.

Constrained and Unconstrained Risk Budgeting / Risk Parity Allocation in Python

A simple Python package for optimizing investment portfolios using historical return data from Yahoo Finance. Users can easily determine the optimal portfolio allocation among a given set of tickers based on the mean-variance optimization method or other algorithms.

Quantum Finance Library

Diffusion-Transformer for Joint Portfolio Construction & Execution Optimization

Markowitz portfolio optimization on synthetic and real stocks

Python financial widgets with okama and Dash (plotly)

Mean Variance (Markowitz) Portfolio Optimization and Beyond